1 We show that a very simple variation on a standard algorithm, Generalized Iterative Scaling (GIS) (Darroch and Ratcliff, 1972), solves this problem.

as we will show, while GIS uses an update rule of the form our modified algorithm uses a rule of the form (3) Note that there are two different styles of model that one can use, especially in the common case that there are two outputs (values for y.)


When there is a maximum with λ k > 0 we know that the partial derivative with respect to λ k will be 0.

this implies that at the optimum, when we discount the observed count by α kthe absolute discounting equation.


Good-Turing discounting has been used or suggested for language modeling several times (Rosenfeld, 1994, page 38), (Lau, 1994).

it has been suggested to use an update of the form where observed[k] * is the Good-Turing discounted value of observed [k].


This update has a problem, as noted by its proponents: the constraints are probably now inconsistent -there is no model that can simultaneously satisfy them.

we note that a simple variation on this update, inspired by the exponential prior, does not have these problems: this can be thought of as picking an α observed[k] for each observed [k].


In fact, we can show that the Backoff version of Kneser-Ney smoothing is an excellent approximation to a maximum entropy model with an exponential prior.

kneser-Ney smoothing is derived by assuming absolute discounting, and then finding the distribution that preserves marginals, i.e.


We were inspired to use an exponential prior by an actual examination of a data set.

we used the grammar-checking data of Banko and Brill (2001).


In general, however, we prefer variations of Generalized Iterative Scaling, which may not converge as quickly, but lead to simpler algorithms.


Source-pair-specific information that statistically characterizes each of the similarity measures is used in a postprocessing step.

we compute statistics from the training data similarity scores for different combinations of source modalities and languages.


Thus, the performance of these systems tends to support the hypothesis that source-pair information, and more specifically, source-pair similarity statistics, contains useful information for the link detection task.

the statistics not only differentiate the source pairs, but provide additional information to the classifier.


Their classes consist of clustered instances like the three shown below: (A) multiple sclerosis, diabetes, osteoporosis, cardiovascular disease, Parkinson's, rheumatoid arthritis, heart disease, asthma, cancer, hypertension, lupus, high blood pressure, arthritis, emphysema, epilepsy, cystic fibrosis, leukemia, hemophilia, Alzheimer, myeloma, glaucoma, schizophrenia, ... A limitation of these concepts is that CBC does not discover their actual names.

cBc discovers a semantic class of canadian provinces such as Manitoba, Alberta, and Ontario, but stops short of labeling the concept as canadian Provinces.


The research discussed above on discovering hyponym relationships all take a bottom up approach.

they use patterns to independently discover semantic relationships of words.


This implicit information, we believed, could serve to augment a small amount of annotated data.

promising were techniques for producing hierarchical clusters at various scales, from small and highly specific to large and more general.


However, we also hoped that a third technique, active learning (Cohn et al., 1996;McCallum and Nigam, 1998), would be particularly effective when used in conjunction with hierarchical word clusters.

active learning attempts to select examples for annotation by estimating the system's certainty about the answer, requesting a human judgment only for those cases where it is most uncertain.


Armed with modern discriminative training methods, it seemed reasonable to us to revisit hierarchical clustering.

we picked up where Spatter left off, with the clustering algorithm of (Brown et al., 1990).


Here we address the problem of unknown phrases.

we show that upon encountering an unknown source phrase, we can substitute a paraphrase for it and then proceed using the translation of that paraphrase.


We carried out SCM disperp experiments for the English-Chinese task, in both directions.

we trained and tested models both for the distortion of English into Chinese-like phrase order, and the distortion of Chinese into English-like phrase order.


The goal is to say whether the hypothesis follows from the text and general background knowledge, according to the intuitions of an intelligent human reader.

the standard is not whether the hypothesis is logically entailed, but whether it can reasonably be inferred.


In our view, the situations described by "order" are more speciÞc than those referred to by "request".

"order" holds in a smaller range of situations than "request".


The results presented in this paper show that SVMs trained with a statistical representation of local context outperform all baselines.

a classifier that relies heavily on local context (very little on dictionaries, and not at all on global context) outperforms classifiers that rely either on global context or dictionaries (but make much less use of local context).


Time sequence scoring is then used to re-rank the candidates and choose the one best temporally aligned with the NE.

we attempt to choose a candidate which is both a good transliteration (according to the current model) and is well aligned with the NE.


We can perform decoding in the mixture model by maximizing the individual node marginals.

to predict y t we compute where p i (y t |x) is computed by first running forward-backward on each of the individual CRFs.


QAP is an NP-hard problem, but in the range of problem sizes that we need to tackle the problem can be solved efficiently.

using standard off-the-shelf integer program solvers, we are able to solve the QAP problems in our experiments in under a second.


An alternative method picks the example with the smallest margin: the difference between the largest two values in the vector q (Abe and Mamitsuka, 1998).

if c and ' c are the two most likely categories for example n x , the margin is measured as follows: In this case Algorithm 1 would rank examples by increasing values of margin, with the smallest value at the top of the ranking.


Therefore, in order to make active learning useful for its applications, both identifying difficult examples and getting good features for these examples are equally important.

a careful treatment of feature design and feature generation is necessary for a successful application of active learning.


They help the machine learning model learn the appropriate sense distribution of the data.

a learning model only using such features will be equal to the "most frequent sense" heuristic used in WSD.


We now look at sentence-level properties.

we analyzed the parsers' behavior on 5,039 sentences in sections 1, 22 and 24 of the Penn treebank.


In particular, we analyzed the parsers' behavior on 5,039 sentences in sections 1, 22 and 24 of the Penn treebank.

we classified each sentence into one of three classes: those where the self-trained parser's f -score increased relative to the baseline parser's f -score, those where the f -score remained the same, and those where the self-trained parser's f -score decreased relative to the baseline parser's f -score.


It did not include the number of unknown words and the number of INs as factors because they did not receive a weight significantly different from zero, and the AIC model search dropped them as factors from the model.

the self-trained parser is more likely to be correct for sentences of length 20-40 and as the number of CCs in the sentence increases.


Contrary to received wisdom, self-training can improve parsing.

we have achieved an absolute improvement of 0.8% over the baseline per-formance.


would be translated into the rule HP →HP HP .

each constituent type found in "S →NP VP ."


The point of parsing at multiple levels of granularity is to prune the results of rough levels before going on to finer levels.

it is necessary for any pruning scheme to retain the true (gold-standard WSJ) constituents in the face of the pruning.


We evaluate both syntactic structure and case structure.

the experimental results for syntactic analysis on web sentences show that the proposed model significantly outperforms known syntactic analyzers.


Given P RE i/j and the lists of categories C RE i/j they belong to, we factorise over all possible category pairs.

we take the cross product of each antecedent and anaphor category to form pairs of 'Wikipedia synsets'.


We therefore discard those responses not present in the key, as we are interested in establishing the upper limit of the improvements given by our semantic features.

we want to define a baseline against which to establish the contribution of the semantic information sources explored here for coreference resolution.


We define an opinion-bearing word as a word that carries a positive or negative sentiment directly such as "good", "bad", "foolish", "virtuous", etc.

this is the smallest unit of opinion that can thereafter be used as a clue for sentence-level or text-level opinion detection.


We rely on the scoring behavior of judges to evaluate the features (section 8) as well as a convenient criterion for evaluating the feasibility of automated scoring based on those features (section 7).

the role of human scorers in this context is to provide a standard for system evaluations (see section 7), as well as to validate specific features and feature classes chosen by the authors (section 8).


To alleviate this problem, we investigate bilingual binarization to factor the synchronous grammar to a smaller branching factor, although it is not guaranteed to be successful for any synchronous rule with arbitrary permutation.

: • We develop a technique called synchronous binarization and devise a fast binarization algorithm such that the resulting rule set allows efficient algorithms for both synchronous parsing and decoding with integrated n-gram language models.


The only extra work we need to do for decoding is to build corresponding target-language (English) subtrees in parallel.

we build synchronous trees when parsing the source-language input, as shown in Figure 1.


This scheme generalizes to the case where we have n nonterminals in a SCFG rule, and the decoder conservatively assumes nothing can be done on language model scoring (because target-language spans are non-contiguous in general) until the real nonterminal has been recognized.

targetlanguage boundary words from each child nonterminal of the rule will be cached in all virtual nonterminals derived from this rule.


12 The model trained on PR05+SYN03 accounts for the most student learning variance in the test data, showing that the training data that is most similar to the test data will yield the highest generalizability.

the combined PR05+SYN03 corpora contains subjects drawn from the same subject pool (2005) as the SYN05 test data, and also contains subjects who interacted with the same tutor voice (synthesized) as this test data.


In this paper we showed that incorporating more information into a representation of the student state has an impact on what actions the tutor should take.

we proposed three metrics to determine the relative weight of the three features.


As an alternative to the feature-based methods, kernel methods (Haussler, 1999) have been proposed to implicitly explore features in a high dimensional space by employing a kernel function to calculate the similarity between two objects directly.

the kernel methods could be very effective at reducing the burden of feature engineering for structured objects in NLP research (Culotta and Sorensen, 2004).


(2) Path-enclosed Tree (PT): It is the smallest common sub-tree including the two entities.

the sub-tree is enclosed by the shortest path linking the two entities in the parse tree (this path is also typically used as the path tree features in the feature-based methods).


However, the cousin relation is only implied by the text and requires additional knowledge to be extracted.

the system requires knowledge of familial relation patterns.


We propose an integrated supervised machine learning method that learns both contextual and relational patterns to extract relations.

we construct a linear-chain conditional random field (Lafferty et al., 2001;Sutton and McCallum, 2006) to extract relations from biographical texts while simultaneously discovering interesting relational patterns that improve extraction performance.


To better gauge how much relational pattern features are affected by errors in the database, we run two additional experiments for which the relational features are fixed to be correct.

imagine that we construct a database from the true labeling of the testing data, and create the relational pattern features from this database.


However, we allow a basic cluster to be added only if it can fill all the columns in that table.


We therefore consider here how to learn models with the least effort.

we argue for a certain kind of semi-supervised learning, which we call prototype-driven learning.


In providing a prototype, however, we generally mean something stronger than a constraint on that word.

we may intend that words which are in some sense similar to a prototype generally be given the same label(s) as that prototype.


Since we have a log-linear formulation, we instead use a gradientbased search.

we use L-BFGS (Liu and Nocedal, 1989), a standard numerical optimization technique, which requires the ability to evaluate L(θ; D) and its gradient at a given θ.


An important property for a probabilistic context-free grammar is that it be consistent, that is, the grammar should assign probability of one to the set of all finite strings or parse trees that it generates.

the grammar should not lose probability mass with strings or trees of infinite length.


One possibility relates to the extreme brevity of the summaries: because the summaries are only 350 words in length, it is possible to have two summaries of the same meeting which are equally good but completely non-overlapping in content.

they both extract informative dialogue acts, but not the same ones.


The analyst is looking for links between Colombian businessmen and paramilitary forces.

the analyst would like to know of evidence that business interests in Colombia are still funding the AUC paramilitary organization.


We hypothesized that system ranks are relatively unstable with respect to individual assessor's judgments.

how well a given system scores is to a large extent dependent on which assessor's judgments one uses for evaluation.


With four or more assessors, the number of questions whose median is zero for the TREC 2003 testset drops to 17; for TREC 2004, 23 for seven or more assessors; for TREC 2005, 27 for nine or more assessors.

f-scores generated using our methodology are far more discriminative.


When finished, we hope our test collection will be a generally useful IR resource.

we expect the collection to be useful for experimentation with citation information, for which there is currently no existing test collection with the properties that ours offers.


Section 5 is dedicated to present experimental results according to different evaluation criteria.

section 5.1 shows the capacity of small pools on identifying relevant documents and section 5.2 illustrates their impact on system comparison; section 5.3 presents statistical validation tests.


In terms of false alarm rate, there are some changes concerning rSVM and MTF.

rSVM at small qrels of less than 100 documents is the best whilst that is MTF qrels of more than 150 documents.


JM smoothing uses a fixed parameter λ to control the interpolation: while the Dirichlet smoothing uses a documentdependent coefficient (parameterized with µ) to control the interpolation: Here P (w|Θ C ) is the probability of word w given by the collection language model Θ C , which is usually estimated using the whole collection of documents C, e.g., P (w|Θ C ) = Recently, the cluster structure of the corpus has been exploited to improve language models for retrieval (Kurland and Lee, 2004;Liu and Croft, 2004).

the cluster-based language model proposed in (Liu and Croft, 2004) uses clustering information to further smooth a document model.


We design experiments to test this hypothesis.

we randomly shrink each document in AP88-89 to a certain percentage of its original length.


However, it is obviously an approximative representation.

there are now conditions under which two word hypotheses can be matched as part of a phrase that were not connected in the original lattice.


The intersection of two finite-state automata produces an automaton with the cross product state set.

if F has m states and G has n states, then F ∩ G has up to mn states (fewer if some of the mn possible states do not lie on any accepting path).


Our decoding algorithm first relaxes the global constraints and solves a simpler problem.

we find the best labeling according to the model, ignoring all the constraints in C. Next, we check whether y * 0 satisifies the constraints.


In what follows, we consider only binary soft constraints-they are either satisfied or violated, and the same penalty is assessed whenever a violation occurs.

∀C ∈ C, ∃w C < 0 such that ∀y, C(y) ∈ {0, w C }.


If there is no constituent in the parse tree that shares the same text span with an argument in the manual annotation, the system cannot possibly get a correct annotation.

the best the system can do is to correctly label all arguments that have a constituent with the same text span in the parse tree.


Given an input sentence, e, the task of semantic parsing is to find a derivation that yields e, f , so that f is a translation of e. Since there may be multiple derivations that yield e (and thus multiple possible translations of e), a mechanism must be devised for discriminating the correct derivation from the incorrect ones.

the semantic parsing model of WASP thus consists of an SCFG, G, and a probabilistic model, parameterized by λ, that takes a possible derivation, d, and returns its likelihood of being correct given an input sentence, e. the output translation, f ⋆ , for a sentence, e, is defined as: where m(d) is the MR string that a derivation d yields, and D(G|e) is the set of all possible derivations of G that yield e. the output MR is the yield of the most probable derivation that yields e in the NL stream.


A simple, principled way to avoid these difficulties is to represent an MR using a sequence of productions used to generate it.

the sequence corresponds to the top-down, left-most derivation of an MR.


However, WASP performs quite favorably compared to SILT and COCKTAIL, which use the same training data.

cOcKTAIL, a deterministic shift-reduce parser based on inductive logic programming, fails to scale up to the ROBOcUP domain where sentences are much longer, and crashes on larger training sets due to memory overflow.


Paraphrases are alternative verbalizations for conveying the same information and are required by many Natural Language Processing (NLP) applications.

summary creation and evaluation methods need to recognize paraphrases and their semantic equivalence.


To evaluate and validate the effectiveness of an automatic evaluation metric, it is necessary to show that automatic evaluations correlate with human assessments highly, positively, and consistently (Lin and Hovy, 2003).

an automatic evaluation procedure should be able to distinguish good and bad summarization systems by assigning scores with close resemblance to humans' assessments.


Using these probabilities, we propose a vector representation where each dimension of a document vector corresponds to a document in the corpus instead of a term in the classical representation.

our document vectors are -dimensional, where is the number of documents in the corpus to be clustered.


However, our generation vectors are -dimensional, where is the number of documents.

vector size is not a constant factor anymore, which causes a problem of scalability to large data sets.


An interesting property of random walks is that for a given node h , y does not depend on @ .

the probability of a random walk ending up at h "in the long run" does not depend on its starting point (Seneta, 1981).


When ¡ s , the random walk goes "out of the cluster" and I q vectors become very dense.

almost all of the graph is reachable from a given node with 4-step or longer random walks (assuming W is around 80), which is an indication of a "small world" effect in generation graphs (Watts and Strogatz, 1998).


Through the label propagation process, our method can make the best of the information of labeled and unlabeled examples to realize a global consistency assumption: similar examples should have similar labels.

the labels of unlabeled examples are determined by considering not only the similarity between labeled and unlabeled examples, but also the similarity between unlabeled examples.


There is also a large body of work on time series analysis and temporal logic in Physics, Economics and Mathematics, providing important techniques and general background information.

this work uses techniques adapted from Seasonal Auto-Regressive Integrated Moving Average models (SARIMA).


We plan to study additional variants that these results suggest may be helpful.

we plan to include more syntactic knowledge and investigate combination techniques at the sentence and subsentence levels.


When training the contrast classifier for each class, it uses the instances in only one class in the labeled data, and implicitly models the data distribution within that class independently of other classes.

given a data instance, the distribution within a class, h j (x), determines the output of the contrast classifier for the class (eq.


Such an independence assumption simplifies the training procedure of the models, but it does not appear to model the factors human beings appear to use in generating summaries.

human summarizers seem to take previous decisions into account when deciding if a sentence in the source document should be in the document's summary.


In this paper, we suggest an alternative simple method for exploring and optimizing the relationship between precision and recall for NER systems.

we describe and evaluate a technique called "extractor tweaking" that optimizes a learned extractor with respect to a specific evaluation metric.


We thus propose to "tweak" a learned NER by varying the single parameter w 0 systematically so as to optimize some user-provided performance metric.

we tune w 0 using a a Gauss-Newton line search, where the objective function is iteratively approximated by quadratics.


In this paper we will experiment with performance metrics based on the (complete) F-measure formula, which combines precision and recall into a single numeric value based on a user-provided parameter β: A value of β > 1 assigns higher importance to recall.

f 2 weights recall twice as much as precision.


This paper presents a multi-modal featurebased system for extracting salient keywords from transcripts of instructional videos.

we propose to extract domain-specific keywords for videos by integrating various cues from linguistic and statistical knowledge, as well as derived sound classes and characteristic visual content types.


In this paper, we propose a system for extracting salient or domain-specific keywords from instructional videos by exploiting joint audio, visual, and text cues.

we first apply a text-based keyword extraction system to find a set of keywords from video transcripts.


This section describes our approach on mining the aforementioned content structure and patterns for instructional videos based on the analysis of both audio and visual information.

given an instructional video, we first apply an audio classification module to partition its audio track into homogeneous audio segments.


Two groups of visual features are then extracted from each segment so as to further derive its content type.

features regarding the presence of human faces are first extracted using a face detector, and these are subsequently applied to determine if the segment contains a narrator.


The audio and visual analysis results are then integrated together to essentially assign a semantic audiovisual label to each video segment.

given a segment, we first identify its major audio type by finding the one that lasts the longest.


Having acquired video content structure and segment content types, we now extract important audiovisual cues that imply the existence of salient keywords.

we observe that topic-specific keywords are more likely appearing in the following scenarios (a.k.a cue context): 1) the first N 1 sentences of segments that contain narrator presentation (i.e.


the corresponding answer); and 5) the first N 2 sentences following the segments that contain silence, or informative text with music.

the first 4 cues conform with our intuition that important content subjects are more likely to be mentioned at the beginning part of narration, presentation, answers, as well as in questions; while the last cue corresponds to the transitional period between topics.


Here, N 1 is a threshold which will be automatically adjusted for each segment during the process.

we set N 1 to min (SS, 3) where SS is the number of sentences that are overlapped with each segment.


Now, given each keyword (K) obtained from GlossEx, we recalculate its salience by considering the following three factors: 1) its original confidence value assigned by GlossEx (C GlossEx (K)); 2) the frequency of the keyword occurring in the aforementioned cue context (F cue (K)); and 3) the number of component words in the keyword (|K|).

we give more weight or incentive (I(K)) to keywords that are originally of high confidence, appear more frequently in cue contexts, and have multiple component words.


The combination is done with a simple majority vote of whether or not constituents should appear in the combined tree.

if a constituent appears at least (m + 1)/2 times in the output of the m parsers, the constituent is added to the final tree.


They assumed that the pronunciation of a word corresponded directly to the sense tag or part-ofspeech of that word.

sense tagging and part-of-speech tagging can determine the reading of a word.


These features ground the pronunciation disambiguation task to the real world through the Web.

they give us knowledge about the problem at hand, i.e., how to read proper names in a real-world context.


This work is based on modelling dialogue as a markov decision process, formalised by a finite state space S, a finite action set A, a set of transition probabilities T and a reward function R. Using this model an optimal dialogue strategy π * is represented by a mapping between the state space and the action set.

for each state s ∈ S this mapping defines its optimal action a * s .


Firstly, the system should execute its dialogue acts in a logical sequence.

the system should greet the user, ask for the slot information, present the query results and then finish the dialogue, in that order (Experiment 1).


Lycos Retriever 1 is something new on the Web: a patent-pending information fusion engine.

unlike a search engine, rather than returning ranked documents links in response to a query, Lycos Retriever categorizes and disambiguates topics, collects documents on the Web relevant to the disambiguated sense of that topic, extracts paragraphs and images from these documents and arranges these into a coherent summary report or background briefing on the topic at something like the level of the first draft of a Wikipedia 2 article.


A subset of the corpus is also hand annotated with ToBI labels.

the experiments in this paper are carried out on 4 speakers similar to (Chen et al., 2004), 2 male and 2 female referred to hereafter as m1b, m2b, f1a and f2b.


Kontrast One reason speakers make entities in an utterance prominence is because of information structure considerations (Rooth, 1992;Vallduví and Vilkuna, 1998).

parts of an utterance which distinguish the information the speaker actually says from the information they could have said, are made salient, e.g.


For simplicity, we only consider the pairwise reordering similarity.

for the source word pair w i and w j , if their aligned positions in the MT hypothesis and a reference are in the same order, we call it a consistent word pair.


Our definition of the case marker prediction task follows Suzuki and Toutanova (2006).

we assume that we are given a source English sentence, and its translation in Japanese which does not include case markers.


Only by taking the transitive closure of these pairwise coreference relations does it become clear that "she" and "Elizabeth (1) " are coreferent.

global training might handle potentially confusing coreference cases better because it allows parameter learning (for each pairwise coreference decision) to be informed by global inference.


We argue that, with appropriate modification to the learning instances, local training is adequate for the coreference resolution task.

we propose that confusing pairs in the training data -such as "she" and "Elizabeth (1) " -be learned as notcoreferent, so long as the global inference step can fix this error by exploiting the structure of the output space, i.e.


[Local Model P (h|x)] For the local model, we define cliques as individual nodes, 4 and parameterize each clique potential as Notice that in this model, finding argmax h P (h|x) corresponds to simply finding argmax INPUT: x, true labeling y * , current local model P (h|x) GOAL: Find the highest confidence labeling y such that y * = single-link-clustering(y ) Figure 1: Algorithm to find the highest confidence labeling y that can be clustered to the true labeling y * [Global Model P (y|h)] For the global model, we assume a deterministic clustering algorithm is given.

we focus on single-link clustering, as it has been shown to be effective for coreference resolution (e.g.


However, the F1-score does not correlate with the error rate.

a lower error rate does not always lead to a higher F1-score, which motivates the Biased Potential Functions that we introduce in the next section.


We hypothesize that using biased potential functions, maximizing the likelihood for training can correlate better with F1-score or MUCscore than the pairwise accuracy.

pairwise MUC e % R % P % F % R % P % F % BASE  we hypothesize that biasing on every coreferent pair will correlate more with F1-score, and biasing on close coreferent pairs will correlate more with MUC-score.


Comparing F1-scores in Table 2 and Table 3, we see that the combination of biased potential functions with SLT improves performance in general.

sLT-P1 3.0 and sLT-Pa 1.5 consistently improve performance over BAsE on both data sets, for both pairwise-F1 and MUC-F1.


In (Soon et al., 2001;Ng and Cardie, 2002b), the problem is treated by thresholding the scores returned by the coreference model.

if the maximum coreference score is below a threshold, then the anaphor is deemed non-referential to any candidate antecedent.


The random variable C is also binary: when C is 1, it means that a new entity {m t } will be created.

the second model measures the probability that m t is not coreferential to any existing entity.


(7) implies that features in the creation model are also separable; Moreover, the conditioning part h k (e, m t ) : e ∈ E t } , also a binary function, only depends on the function values of the set of link features {g 1k (e, m t ) : e ∈ E t } (for some k).

once {g 1k (e, m t ) : e ∈ E t } and C are known, we can compute h i (E t , m t , C) without actually comparing m t with any entity in E t .


We will concentrate on features used in the creation model since those in the link model can be found in the literature (Soon et al., 2001;Ng and Cardie, 2002b;Yang et al., 2003;Luo et al., 2004).

we show how features in the creation model can be computed from a set of feature values from the link model for a few example categories.


As a simple example, consider prohibiting coreferent sets that consist only of pronouns.

we would like to require that there be at least one antecedent for a set of pronouns.


To address this problem, we propose constructing training examples consisting of one negative exam- ple and one "nearby" positive example.

when agglomerative clustering incorrectly merges two clusters, we select the resulting cluster as the negative example, and select as the positive example a cluster that can be created by merging other existing clusters.


We introduce a novel ranking algorithm called GRASSHOPPER, which ranks items with an emphasis on diversity.

the top items should be different from each other in order to have a broad coverage of the whole item set.


Instead, the top results should be different and complementary.

one wants 'subtopic diversity' in retrieval results (Zhai et al., 2003).


It is known that the fundamental matrix gives the expected number of visits in the absorbing random walk (Doyle and Snell, 1984).

n ij is the expected number of visits to state j before absorption, if the random walk started at state i.


To create a sparse graph, we use the cosine threshold value of 0.1 obtained in (Erkan and Radev, 2004).

the edge weight between sentence vectors s i and s j is defined as The second input for GRASSHOPPER is an initial ranking distribution, which we derive from the position of each sentence in its originating document.


for each of the 30 DUC 2003 Task 2 document sets, we computed ROUGE-1 scores comparing our generated summary to 4 model summaries.


Figure 3(a) shows that country coverage grows much more rapidly for GRASSHOPPER than for MOVIECOUNT.

we see more comedians from around the world ranked highly by GRASSHOPPER.


When the hitcount is high, RESOLVER can prevent the merge.

the Coordination-Phrase Filter searches for hitcounts of the phrase "s 1 and s 2 ", where s 1 and s 2 are a candidate pair for merging.


To show the validity of the domain restriction we conducted a preliminary experiment, contrasting the probability for two words to be related in Word-Net (Magnini and Cavaglià, 2000) with their domain similarity, measured in the Domain Space induced from the British National Corpus.

for each couple of words, we estimated the domain similarity, and we collected word pairs in sets characterized by different ranges of similarity (e.g.


Results in Figure 1 show a monotonic crescent relation between these two quantities.

the probability for two words to be related tends to 0 when their similarity is negative (i.e., they are not domain related), supporting the basic hypothesis of this work.


The dependencies among variables in a PCFG are depicted graphically in Figure 1, which makes clear that the Gibbs sampler is highly parallelizable (just like the EM algorithm).

the parses t i are independent given θ and so can be sampled in parallel from the following distribution as described in the next section.


A Hastings sampler for a probability distribution π(s) is an MCMC algorithm that makes use of a proposal distribution Q(s ′ |s) from which it draws samples, and uses an acceptance/rejection scheme to define a transition kernel with the desired distribution π(s).

given the current state s, a sample s ′ = s drawn from Q(s ′ |s) is accepted as the next state with probability and with probability 1 − A(s, s ′ ) the proposal is rejected and the next state is the current state s. We use a component-wise proposal distribution, generating new proposed values for t i , where i is chosen at random.


This paper introduces an unsupervised morphological segmentation algorithm that shows robust performance for four languages with different levels of morphological complexity.

our algorithm outperforms Goldsmith's Linguistica and Creutz and Lagus's Morphessor for English and Bengali, and achieves performance that is comparable to the best results for all three PASCAL evaluation datasets.


There is another class of unsupervised morphological learning algorithms whose design is driven by the Minimum Description Length (MDL) principle.

eM is used to iteratively segment a list of words using some predefined heuristics until the length of the morphological grammar converges to a minimum.


Consequently, given two suffixes x and y, our method posits xy as a composite suffix if xy and x are similar in terms of the words to which they attach.

we consider xy and x to be similar if their similarity value as computed by the formula below is greater than 0.6: where |W ¢ | is the number of distinct words that combine with both xy and x, and |W| is the number of distinct words that combine with xy.


the frequency ratio of w to r is less than one).

we hypothesize that the inflectional or derivational forms of a root word occur less frequently in a corpus than the root itself.


To address this sparseness problem, we rely on both relative corpus frequency and suffix level similarity to identify incorrect attachments.

if the WRFR of a <word, root> pair is between 1 and 10, we determine that an attachment to the root is incorrect if -WRFR + * (suffix level similarity) < 0, where is set to 0.15.


So, we check whether each of the learned rules occurs frequently enough for all the <allomorph, root> pairs associated with a suffix, with the goal of filtering the low-frequency orthographic rules.

for each suffix ¡ , we repeat the following steps: (a) Counting the frequency of rules.


Finally, we adapt our candidate allomorph detection method described above to induce allomorphs that are generated through orthographic changes of edit distance greater than 1.

if ¡ is a word in the induced root list (e.g.


8 After inducing the morphemes, we can use them to segment a word w in the test set.

we (1) generate all possible segmentations of w using only the induced affixes and roots, and (2) apply a sequence of tests to remove candidate segmentations until we are left with only one candidate, which we take to be the final segmentation of w. Our first test involves removing any candidate segmentation m 1 m 2 … m n that violates any of the linguistic constraints below: • At least one of m 1 , m 2 , …, m n is a root.


Results of our segmentation algorithm are shown in rows 3-6 of Table 3.

row 3 shows the results of our basic segmentation system as described in Section 3.


We have presented four tactical generation systems based on various SMT-based methods.

the hybrid system produced by inverting the WASP semantic parser shows the best overall results across different application domains.


For a given SCFG production l → α f , α c , we ask, given the source RHS α f that is assumed given (e.g., provided by a syntactic parser), which of its RHS elements are also present in α c .

we write: where k h , k i l , k j r ('k' for keep) are binary variables that are true if and only if constituents H, L i , R j (respectively) of the source RHS α f are present in the target side α c .


To alleviate this problem, the remove-one method is used for testing the knowledge-based models.

the models are re-trained for each test word using information about all the words in Cilin except the test word.


Next, local and topical features are extracted from the context of the ambiguous word.

we use the current word and its part-of-speech, a local context of three words to the left and right of the ambiguous word, the parts-of-speech of the surrounding words, the verb and noun before and after the ambiguous words, and a global context implemented through sense-specific keywords determined as a list of at most five words occurring at least three times in the contexts defining a certain word sense.


It is well-known that an average is easily skewed by outliers.

the existence of one or more terms that have low mutual information with every other term could potentially distort results.


Both of these formulations achieve significant performance gains over the base classifier.

the joint model achieves f -score improvements of 3.7-5.3% on three datasets.


Model parameters are estimated using maximum entropy (Berger et al., 1996).

we estimate parameters with the limited memory variable metric algorithm implemented in the Toolkit for Advanced Discriminative Modeling 1 (Malouf, 2002).


Interestingly, they find a degredation in performance.

they obtain significant improvements in precision, but with larger losses in recall (especially for proper names and common nouns).


The task for the anaphoricity determination component is the following: one wants to decide for each mention i in a document whether i is anaphoric or not.

this task can be performed using a simple binary classifier with two outcomes: ANAPH and ¬ANAPH.


To demonstrate the inherent limitations of cascading, we also give results for an oracle system, ORACLE-LINK, which assumes perfect linkage.

it always picks the correct antecedent for an anaphor.


The integer programming formulation we provide here has qualities which address both of these issues.

we define two objective functions for coreference resolution to be optimized with ILP.


We have provided two ILP formulations for resolving coreference and demonstrated their superiority to a pairwise classifier that makes its coreference assignments greedily.

we have also shown that ILP provides a natural means to express the use of both anaphoricity classification and coreference classification in a single system, and that doing so provides even further performance improvements, specifically f -score improvements of 5.3%, 4.9%, and 3.7% over the base coreference classifier on the ACE datasets.


Instead, user interfaces which organize and group retrieval results have been shown to be helpful for and preferred by users over the straight results-list model when engaged in exploratory tasks (Yee et al., 2003;Pratt et al., 1999;Kaki, 2005).

a representation known as hierarchical faceted metadata is gaining great traction within the information architecture and enterprise search communities (Yee et al., 2003;Weinberger, 2005).


For two terms x and y, x is said to subsume y if the following conditions hold: .

x subsumes y and is a parent of y, if the documents which contain y, are a subset of the documents which contain x.


The parameterizations that we focused on have to do with the information-theoretic objectives in the two unsupervised algorithms.

following (Karakos et al., 2007b), we replace the mutual information quantities in IDTs as well as sIB with the parameterized mutual information measures mentioned above.


3 • We train a "meta-classifier" that predicts the true rank (or accuracy) of each clustering based on the feature vector of the clustering.

for each task (Binary, Multi5, and Multi10) and each clustering method (sIB and IDT), a meta-classifier is learned thus: • We obtain 10 clusterings (α = 0.1, 0.2, .


This time, however, we carefully select |A|/L documents from each newsgroup rather than randomly choosing 500/L of them.

for each test example (document) X ∈ A, we add a similar training example X into A , chosen as follows: We associate each test example X to the most similar of the L training newsgroups, under a constraint that only |A|/L training examples may be associated to each newsgroup.


Removing all non-rationale text from the training documents (S5) was even worse than removing the rationales (S3).

we cannot hope to do well simply by training on just the rationales (S5), although that approach is improved somewhat in S4 by treating each rationale (similarly to S1) as a separate SVM training example.


What is an annotator actually doing when she annotates rationales?

how do her rationales derive from the true value of θ and thereby tell us about θ?


Basically, a model (M1) is better than another (M2) if M1's lower bound is greater than the upper bound of M2.

one knows that 95% of the time, the worst case situation of M1 (the lower bound) will always yield a higher reward than the best case of M2.


Our goal is to use the domain specific information about the salient entities on a graphical display, as indicated by the user's eye gaze, to help recognition of the user's utterances.

we incorporate this salient domain information in speech recognition via salience driven language modeling.


However, positivenegative disagreement is observed for only 0.7% of the data.

this statistics means that the task of extracting neutral examples, which has hardly been explored, is intrinsically difficult.


We demonstrate that the agreement-based joint model is more expressive than individual ranking models.

every training set that can be perfectly ranked by individual ranking models for each aspect can also be perfectly ranked with our joint model.


For each training instance (x t , y t ), each aspect i ∈ 1...m, and each rank r ∈ 1...k, define an auxiliary variable r indicates whether the true rank y[i] t is to the right or left of a potential rank r. Now suppose that a training set (x 1 , y 1 ), ..., (x T , y T ) is perfectly rankable for each aspect independently.

for each aspect i ∈ 1...m, there exists some ideal model ) such that the signed distance from the prediction to the r th boundary: r has the same sign as the auxiliary variable y[i] t r .


That is, for each aspect i ∈ 1...m, there exists some ideal model ) such that the signed distance from the prediction to the r th boundary: r has the same sign as the auxiliary variable y[i] t r .

the minimum margin over all training instances and ranks, , is no less than zero.


m(S) is the number of "missing" links in the system annotation relative to the co-reference class as marked up in the model.

this is the minimum number of co-reference links that need to be added to the system annotation to fully generate the co-reference class S in the model.


(J)accard: Two sets A and B have distance J = 1 − |A ∩ B|/|A ∪ B|.

the distance between two sets is larger, the smaller their intersection and the larger their union.


We believe that similar improvements can be achieved on other discourse annotation tasks in the scientific literature domain.

we plan to investigate the use of scientific attribution information for the citation function classification task.


likelihood of containing "to-do" or action-items can alleviate a user's time burden and is the subject of ongoing research throughout the literature.

an e-mail user may not always process all e-mails, but even when one does, some emails are likely to be of greater response urgency than others.


Although we do not have true log-odds, we can introduce local density models to estimate the local sensitivity of the model.

we introduce a series of reliability indicators by first defining a ∆ distribution and either computing .


Since we are concerned with how the decision function would change as we move locally around the current prediction point, it is natural to consider a set of shifts defined by the k neighbors.

let d i denote the document that has been shifted by a factor β i toward the ith neighbor, i.e.


In order to produce a ranking score, the confidence that the document contains an action-item is: where s is a sentence in document d, π is the classifier's 1/0 prediction,λ is the score the classifier assigns as its confidence that π(s) = 1, and n(d) is the greater of 1 and the number of (unigram) tokens in the document.

when any sentence is predicted positive, the document score is the length normalized sum of the sentence scores above threshold.


: Boeing, Heinz, Staples, Textron Test : General Electric, General Motors, Gannett, The Home Depot, IBM, Kroger, Sears, UPS Ground truth was created from the entire web, but since the corpus for each company is only a small web snapshot, the experimental results are not similar to extraction tasks like MUC and ACE in that the corpus is not guaranteed to contain the information necessary to build the entire database.

7 One thing to note is that since all relationships are given confidence estimates separately, this process may result ultimately in a database where constraints are violated.


1 The queries generated this way are then submitted to an IR engine to obtain document counts.

we indexed the BNC using GLIMPSE (Global Implicit Search) a fast and flexible indexing and query system 2 (Manber and Wu, 1994).


(2004) and performs domain driven disambiguation.

the approach compares the domain of the context surrounding the target word with the domains of its senses and uses a version of WordNet P R BaseR 23.1 # †$ ‡ 22.7 # †$ ‡ BaseS 36.6 * †$ ‡ 35.9 * †$ ‡ McCarthy 49.0 * #$ ‡ 43.0 * #$ ‡ IR-Model 58.0 * # † ‡ 57.0 * # † ‡ IRST-DDD 58.3 * # † ‡ 58.2 * # † ‡ Semcor 62.4 * # †$ 62.4 * # †$ GAMBLE 65.1 * # †$ ‡ 65.2 * # †$ ‡  augmented with domain labels (e.g., economy, geography).


Instead, we validate the algorithms by deleting selected words from sample sentences, to see whether the algorithms can restore the missing words.

we create a lexical gap and evaluate the ability of the algorithms to fill the gap.


Moreover we retain only the sentences in which at least one of the context words is in our previously acquired knowledge-base of near-synonym collocations.

the anti-collocations method works only if we know how a word in the context collocates with the near-synonyms from a group.


when using the 500 word features for each training example, only the Naïve Bayes algorithm was able to run in reasonable time.


The goal of transliteration model is to learn high-quality transliteration blocks from the training data in a unsupervised fashion.

a block X can be represented by its left and right boundaries in the source and target NEs shown in Figure 2: where f j+l j is the source letter-ngram with (l + 1) letters in source language, and its projection of e i +k i in the English NE with left boundary at the position of i, and right boundary at (i + k).


We attempt to discover explanations for these differences.

the focus is on the analysis of OOVs: A perceived strength of subword models, when contrasted with word models, is that subword models can generalize to previously unseen word forms by recognizing them as sequences of shorter familiar word fragments.


More or less, there is a one-to-one correspondence between letters and phonemes.

the spelling of a word indicates the pronunciation of the word, and when splitting the word into parts, the pronunciation of the parts in isolation does not differ much from the pronunciation of the parts in context.


Approaches to dependency parsing either generate such trees by considering all possible spanning trees (McDonald et al., 2005), or build a single tree on the fly by means of shift-reduce parsing actions (Yamada & Matsumoto, 2003).

nivre and Scholz (2004) and Attardi (2006) have developed deterministic dependency parsers with linear complexity, suitable for processing large amounts of text, as required, for example, in information retrieval applications.


Given a dependency graph G = (s, A), for a sentence s = w 1 , ..., w n , the revised tree is R(G) = (s, A ), where each dependency a i is equal to F (a i ).

the head in a i has been changed, or not, according to the rule predicted by the classifier.


In other words, the head in a i has been changed, or not, according to the rule predicted by the classifier.

we assume that revisions are independent of each other and perform a revision of a tree from left to right.


With respect to transformation-based methods, our method does not attempt to build a tree but only to revise it.

it defines a different output space from the base parser's: the possible revisions on the graph.


Previous work has shown that high-quality unlexicalized PCFGs can be learned from a treebank, either by manual annotation (Klein and Manning, 2003) or automatic state splitting (Matsuzaki et al., 2005;Petrov et al., 2006).


In our case, we also require the projections to be sequentially compatible, so that π i→j = π k→j •π i→k .

each projection is itself a coarsening of the previous projections.


That is, each projection is itself a coarsening of the previous projections.

we take the projection π i→j to be the map that collapses split symbols in round i to their earlier identities in round j.


Fortunately, there is a well worked-out notion of estimating a grammar from an infinite distribution over trees (Corazza and Satta, 2006).

we can estimate parameters for a projected grammar π(G) from the tree distribution induced by G (which can itself be estimated in any manner).


1 shows the tremendous reduction in parsing time (all times are cumulative) and gives an overview over grammar sizes and parsing accuracies.

in our Java implementation on a 3GHz processor, it is possible to parse the 1578 development set sentences (of length 40 or less) in less than 1200 seconds with an F 1 of 91.2% (no search errors), or, by pruning more, in 680 seconds at 91.1%.


We applied our model directly to each of the treebanks, without any language dependent modifications.

the same model hyperparameters (merging percentage and smoothing factor) were used in all experiments.


Hence, the following is an admissible additive heuristic (Felner et al., 2004), where h * i (s) denote the optimal completion costs in the projected search graph π i (G).

the completion cost of a state bounds the sum of the completion costs in each projection.


We enrich our procedure by expressing the projected configuration costs as linear functions of features.

we define feature vectors f c (r) and f d (h, t, h t ) over the CFG and dependency projections, and intro- duce corresponding weight vectors w c and w d .


We model two RSRs, Cause and Contrast, adopting the definitions of Marcu and Echihabi (2002) (henceforth M&E) for their Cause-Explanation-Evidence and Contrast relations, respectively.

we follow their intuition that in building an automated model it is best to adopt a higher-level view of relations (cf.


To informally test this, we tested against explicitly cue-phrase marked examples gathered from PDTB.

we used the M&E-style method for mining instances, but we gathered them from the PDTB corpus.


The intuition here is that all sentence boundaries should not be treated equally during RSR instance mining.

we would like to make our patterns recognize that some sentence boundaries indicate merely an orthographic break without a switch in topic, while others can separate quite distinct topics.


We define the measurement of accuracy here in terms of wordlevel precision/recall.

the set of words filtered by our heuristics are compared to the "correct"  words to cut, i.e.


In real documents, each sentence tends to contain only a few nouns, and even fewer of them can fill roles like subject and object.

nouns compete with each other for the available syntactic positions in sentences; once one noun is chosen as the subject, the probability that any other will also become a subject (of a different subclause of the same sentence) is drastically lowered.


can be used to achieve a running time of roughly O(2 k n 2 ).

the path length k only exponentiates a small constant, instead of the problem size n, while the dependence on n is in fact quadratic.


We contend that such a resource allows one to develop computational artifacts, such as grammars and transfer rules, which can be used as "seed" knowledge for building larger resources.

knowing a little about the structure of a language can help in developing annotated corpora and tools, since a little knowledge can go a long way in inducing accurate structure and annotations (Haghighi and Klein, 2006).


2 (1) Rhoddodd yr athro lyfr i'r bachgen ddoe gave-3sg the teacher book to-the boy yesterday "The teacher gave a book to the boy yesterday" (Bailyn, 2001) Although IGT is usually embedded in linguistics documents as part of a larger analysis, in and of itself it contains analysis and interesting information about the source language.

the gloss line, which is word and morpheme aligned with the source, contains word and morpheme translations for the source language data, and can even contain grammatically salient annotations (e.g., 3sg for Third Person Singular).


First language acquisition begins at infancy, and the primary grammatical structures of the target language are acquired by age four in typically developing chil-dren (Bates, 2003).

most grammar is acquired prior to the beginning of a child's formal education.


That all grades are equally likely a priori.

these may be poor assumptions in a real application, but they can be easily included or excluded in the model as desired.


The phrase translation strategy means that we directly construct a French-German phrase translation We assume that these French-English and English-German tables are built using the phrase model training code in the baseline system described in the introduction.

phrases are heuristically extracted from word-level alignments produced by doing GIZA++ training on the corresponding parallel corpora (Koehn et al., 2003).


The PhraseTrans SMT systems significantly outperformed the SntTrans15 and SntTrans1 systems.

the phrase translation strategy is better than the sentence translation strategy.


Therefore, there doesn't seem to be any good reason why a machine translation system could not handle the post-editing task.

given such data as described in Section 2.2, the idea of using a statistical MT system for post-editing is appealing.


The first observation in these results is that there is nothing to be gained from post-editing when both the translation and APE systems are trained on the same data sets (Portage Job Bank + Portage APE experiments).

the translation system is apparently already making the best possible use of the training data, and additional layers do not help (but nor do they hurt, interestingly).


the answer identification accuracy is limited because the correct answer shares its units with other numerical entities in the answer documents.


Hence, we stress on either head or tail fragments of the passage.

the passage score is determined by computing equation 1for head and tail parts of passage.


By exploiting the mapping between PropBank and VerbNet, we can transform the data to make it more consistent, and to expand the size and variety of the training data.

we can use the mapping to transform the verb-specific PropBank role labels into the more general thematic role labels that are used by VerbNet.


However, it is important to note that these performance figures are not directly comparable, since the two systems are performing different tasks: The Original system labels Arg0-5,ArgA and ArgM and the Mapped system labels Arg0, Arg1, ArgA, ArgM and Group1-5.

the role labels generated by the original system are verb-specific, while the role labels generated by the new system are less verb-dependent.


This suggests that the features it uses are being over-fit to the training data and are more idiosyncratic to a given dataset.

the predicate whose arguments are being identified, and the head word of the syntactic constituent being classified are both important features in the task of argument classification.


The term signature wants to capture the notion that the data it embodies is truly representative of a particular item, and that shows the details of its typical behavior.

we wanted linguistic cues appearing in different occurrences of the same word to be observed as related information.


We wish to find the kbest matches for a substring, s, in a dictinoary, dict.

grep s dict | sort-n | head-k, but we would like to do this in sublinear time.


Some of the 488 documents available in EU-ROPARL were missing in Finnish, Swedish, Greek and Danish.

greek had 392 documents, Danish had 487 documents, and Swedish and Finnish had 433 each.


All of the incorrect results can be traced to mistranslation, or to missing/truncated documents.

one of the documents is severely truncated in Danish and English, one of the German documents missing a portion of its text, and the Spanish version of one of the documents contains a number of phrases and sentences of English, apparently belonging to the English version of the text.


The AMC similarity measure, which relies on syntactic as well as lexical similarity, produced somewhat different results from simpler bag-of-word or n-gram measures.

there was a difference in behavior between native and non-native speakers: non-native speakers showed increased structural similarity to the lecture with increasing scores, but native speakers did not.


This work extends the current body of research on non-projective dependency structures in natural language.

we offer new insights into formal properties of non-projective edges that, if possible, both provide adequate means for linguistic description and at the same time are useful as features in machine-learning approaches.


Finite-state models constitute an important framework both in syntactic pattern recognition and in language processing.

stochastic finitestate transducers (SFSTs) have proved to be useful for machine translation tasks within restricted domains; they usually offer high speed during the decoding step and they provide competitive results in terms of error rates (Mohri et al., 2002).


With regard to the speech test, the input consisted of the speech signal recorded by 36 speakers, each one reading out 50 sentences from the test-set in Table 1.

each sentence was read out by at least three speakers.


This can be explained by the fact that in the phrasebased approach, the same translation can be hypothesized following several segmentations of the input sentence, as phrases appear (and are collected) from multiple segmentations of the training sentence pairs.

the search graph seems to be overpopulated under the phrase-based approach.


This study investigates whether some speech recognition (SR) errors are easier to detect and what patterns can be identified from those errors.

sR errors were examined from both nonlinguistic and linguistic perspectives.


Eight paragraphs were randomly selected from the transcripts of two task scenarios based on two criteria: recognition accuracy and paragraph length (measured by # of words).

the overall recognition accuracy (84%) and the length of a medium-sized paragraph (90 words) of the corpus were used as references.


One way ANOVA revealed a significant effect of error sequence on ease of detection, p < 0.05.

isolated errors (mean = 2.33) are easier to detect than consecutive errors (mean = 1.97).


One way ANOVA results yielded a significant effect of POS change on ease of detection (p < 0.05).

it was easier to detect errors that had different POSes (mean = 2.32) from their references than those that shared the same POSes (mean = 1.91).


We are collaborating with researchers in molecular biology with the goal of automatically extracting BPLs from text with BioNLP techniques, to expand their protein localization database, namely PSORTdb 2 (Rey et al., 2005).

the task is to produce as output the relation tuple BPL(BACTERIUM, PRO-TEIN, LOCATION) along with source sentence and document references.


The BPL relation can be predicted by a fusion of BP and PL once they are extracted.

a BP and a PL that are extracted from the same sentence are merged into a BPL.


Figures 1 and 2 indicate that there were significant differences (at α = 0.01) in the methods in terms of the rate at which keys were pressed.

while overall communication rate was significantly faster with advanced prediction, users took 0.641 seconds longer for each key press from using advanced prediction compared to entry without prediction.


The keystroke savings that users of basic prediction achieved seems quite a bit lower than the potential keystroke savings offered by the predictions.

the prediction utilization of basic prediction was much lower than that of advanced prediction.


A second reason could be related to the consistency of the basic prediction method.

at least some subjects using the basic prediction method learned to scan the prediction list when the desired word was recently used and mentioned it in the exit survey.


Furthermore, even all the text of the article is not enough for classification at times.

note Example 5, where unless you know whether IBM was the world leader or simply one of the world leaders at the time of the article, no additional context would help.


This paper presents empirical results that contradict the prevailing opinion that entity extraction is a boring solved problem.

we consider data sets that resemble familiar MUC/ACE data, and report surprisingly poor performance for both commercial and research systems.


In this paper, we report on the more surprising result that transfer issues arise even for texts with closely aligned content or closely aligned styles.

we consider a range of primarily business-related texts that are, on the face of it, close in style and/or substance to the journalistic stories in existing NE data sets, MUC 6 in particular.


In the practical learning process, only the inner products of the feature vectors are needed (see Section 2.2, 2.3 and 3), so we can perform the so-called kernel trick to avoid dealing with the very highdimensional feature vectors explicitly.

for x, z ∈ X , a kernel function is defined as: In our case, the blended n-spectrum string kernel (Lodhi et al., 2002) that compares two strings by counting how many (contiguous) substrings of length from 1 up to n they have in common, is a good choice for the kernel function to induce our feature spaces H x and H y implicitly, even though it brings in some uninformative features (word n-grams) as well, when compared to our original definition.


As similar items should be in the same part of the split, the best split is one which removes edges with low weights.

a minimum cut problem is to find a partition of the graph which minimizes the following formula, where w(u, v) expresses the weight of an edge between two vertices.


The mincut algorithm will put vertices "religious" and "scrupulous" in the same cut (subjective category) as this results in the least cost 0.93 (ignoring the cost of assigning the unrelated sense of "flicker").

the edges between the vertices are likely to correct some initially wrong classification and pull the vertices into the right cuts.


Since we have shown evidence that PRAGUE and BCLKG do not scale well to large datasets, we also compared against ORACLE and CENTROID in a large-scale setting.

we compare to the experimental setup on 64 modern languages used to reconstruct POc described before.


While Dirichlet priors over grammar probabilities make learning algorithms easy, they are limiting.

as noted by Blei and Lafferty (2006), there is no explicit flexible way for the Dirichlet's parameters to encode beliefs about covariance between the probabilities of two events.


While empirical Bayes learning permits these covariances to be discovered without supervision, we found that by initializing the covariance to encode beliefs about which grammar probabilities should covary, further improvements were possible.

we grouped the Penn Treebank part-of-speech tags into coarse groups based on the treebank annotation guidelines and biased the initial covariance matrix for each child distribution θ c (• | •, •) so that the probabilities of child tags from the same coarse group covaried.


now define the distribution over superlingual tags and their associated distributions on parts-of-speech.

each superlingual tag z ∈ N is drawn with probability π z , and indexes the set of distributions ω ℓ 1 z , .


It also leads to a desirable property: for a tag to have high probability each of the incoming distributions must allow it.

any expert can "veto" a potential tag by assigning it low probability, generally leading to consensus decisions.


The domains of locality of TL-MCTAG and SL-MCTAG (and trivially, TAG) can be thought of as lexically defined.

all locations at which the adjunction of one tree set into another may occur must be present within a single lexical item.


Let f (t, r) denote the number of times rule r appears in tree t, and let f (t,r) = r∈r f (t, r).

we see that the complete data likelihood is the likelihood is a product of multinomials, one for each nonterminal equivalence class, and there are no constraints placed on the parameters of these multinomials besides being positive and summing to one.


The Dirichlet has the density function: Thus the prior overθ is a product of Dirichlets,which is conjugate to the PCFG likelihood function .

the posterior P (θ|s, t, α) is also a product of Dirichlets, also factoring into a Dirichlet for each nonterminalN , where the parameters αr are augmented by the number of times rulē r is observed in tree t: We can see that αr acts as a pseudocount of the number of timesr is observed prior to t. To make use of this prior, we use the Variational Bayes (VB) technique for PCFGs with Dirichlet Priors presented by Kurihara and Sato (2004).


Many previous studies, such as (Callison-Burch et al., 2006), have pointed out drawbacks of BLEU, and any other similarity measure could be utilized instead.

similarity measures that model aspects of sentences that are ill handled by standard phrase-based decoders (such as syntactic structure or semantic information) could be useful here.


Most of the previous triangulation approaches (Kumar et al., 2007;Cohn and Lapata, 2007;Filali and Bilmes, 2005;Simard, 1999;Och and Ney, 2001) add information obtained from a third language.

they work with the union of the data from the different languages.


The way a decoder constructs translation hypotheses is directly related to the weights for different model features in a SMT system, which are usually optimized for a given set of models with minimum error rate training (MERT) (Och, 2003) to achieve better translation performance.

the weights obtained for a model do not necessarily apply to another model.


Our algorithm is surprisingly accurate given that it is quite resource-and knowledge-lean.

it relies only on Ladusaw's hypothesis as initial inspiration, a relatively short and arguably noisy list of NPIs, and a large unannotated corpus.


An example NPI is 'anymore': one can say 'We don't have those anymore' but not '¦We have those anymore'.

we propose to take advantage of the seminal hypothesis of Ladusaw (1980, influenced by Fauconnier (1975, inter alia): (Ladusaw) NPIs only appear within the scope of downward-entailing operators.


We choose to focus on the items on these lists that seem most likely to be effective cues for our task.

we select a subset of the Lawler NPIs, focusing mostly on those that do not have a relatively frequent non-NPI sense.


We deal with this problem by introducing distractors -items that are not produced by our algorithm, but are similar enough to not be easily identifiable as "fakes".

for each possible part of speech of each of our system's outputs c that exists in WordNet, we choose a distractor that is either in a "sibling" synset (a hyponym of c's hypernym) or an antonym.


We follow Charniak (2001) in assuming that two names are consistent (can be references to the same entity) if they do not have different expansions for any lexical symbol.

a particular entity E P ER,Clinton has a title E 0 P ER,Clinton = "President", a first name E 1 P ER,Clinton = "Bill" etc.


These restrictions mean that we do not compute the exact posterior.

the actual model allows entities to contain examples with no words in common, but our search procedure does not explore these solutions.


Apart from the constraint that the parameters should sum to one, the Dirichlet priors in the HDD model do not impose any dependency among the parameters of the resulting multinomial.

the document models cannot capture the notion that related terms tend to co-occur together.


Pcluster captures more relevant semantic relationships by instead associating each word with the documents in which it appears.

each word is associated with a binary vector indexed by documents in the corpus, where a 1 means the word appears in the corresponding document.


These α k 's impose cor-  relations on the probabilities of the children under k in an interesting fashion.

if we compare α k to θ 0k α parent(k) , then a larger value of α k implies that the probabilities of picking one of the children of k (from among all nodes) are positively correlated, while a smaller value of α k implies negative correlation.


We accomplish this goal by ranking the set of utterances by our confidence that they contain the query word, a task known as Ranked Utterance Retrieval (RUR).

we are interested in the case when the user's query word can not be anticipated by a Large Vocabulary Continuous Speech Recognizer's (LVCSR) decoding dictionary, so that the word is said to be Out-Of-Vocabulary (OOV).


Because of speaker variation and because recognition is errorful, we ought to also consider non-lexical degradations of the query phoneme sequence.

we should incorporate P (H|Q) in our ranking function.


Since we don't want to sum over all possible recognition hypotheses H, we might instead sum over the smallest set H such that H∈H P (H|Q) ≥ γ.

we could take the most probable degradations until their cumulative probability exceeds some threshold γ.


While some of these deficiencies could be addressed with an extension to CMQD (e.g., by expanding the degradation lattices to include language model scores), we can do better using a more powerful modeling framework.

we adopt the approach of phrase-based statistical machine translation (Koehn et al., 2003;Koehn and Hoang, 2007).


For example, if we have never seen the word houses in language model training, but have examples of house, we still can expect houses are to be more probable than houses fly.

factors allow us to collect improved statistics on sparse data.


We note, however, that since these are not true phonemes (but rather phonemes copied over from pronunciation dictionaries and word transcripts), we must cautiously interpret these results.

it seems reasonable that this framework will overestimate the strength of the word based system.


As a second method for handling recognition errors, we also considered variants of our ranking function.

we incorporated query degradations hypothesized using factored phrase-based machine translation.


Comparing the MAP for PBQD-Fac with MAP using the generative baseline for the most improved indexing system (the word system), we find that this degradation approach again statistically significantly improved MAP.

these two strategies for handling recognition errors in RUR appear to work well in combination.


Models of query correction in previous work were trained and evaluated using manually created querycandidate pairs.

human annotators were given a set of queries and were asked to provide a correction for each query when it needed to be rewritten.


Our approach draws on previous research in English spelling and query correction, Japanese katakana variation and transliteration, and semantic similarity, and builds a model that makes improvements over previously proposed query correction methods.

the use of orthographically augmented semantic kernels proposed in this paper is general and applicable to other languages, including English, for query alteration, especially when the data sparseness is an issue.


When some or all priors are excluded, the F1 score drops substantially (over 10 points in all cases, and over 40 points in some).

excluding the corpus prior, as in NO-PRIOR and NO-COR-PR, results in oversegmentation, as is evident from the high recalls and low precisions.


Defining the translation function as finding the best derivation has the unfortunate side effect of forcing differently-derived versions of the same target sentence to compete with each other.

the true score of each translation is "fragmented" across many derivations, so that each translation's most probable derivation is the only one that matters.


Each sequences of consecutive phrases, mapping to cells in a CKY matrix, are then translated through a bilingual phrase table and scored as implemented in (Koehn et al., 2005;Chiang, 2005).

their system shares the same phrase table with standard phrase-based SMT systems.


Asterisks in Figure 2 denote the labels that differ significantly between U.S. and non-U.S. wishes.

3 we observed that there are significantly more wishes about love, peace, and travel from non-U.S. locales, and more about religion from the U.S.


Another simple method for detecting wishes is to train a standard word-based text classifier using the labeled training set in the target domain.

we represent each sentence as a binary word-indicator vector, normalized to sum to 1.


Based on such considerations, we devised the following scheme for scoring templates: where in(t) is the in-degree of node t, defined as the sum of edge weights coming into t; out(t) is the outdegree of node t, defined similarly.

a template receives a high score if it is "used" by many frequent wishes but does not match many frequent content-only wishes.


We consider a text regression problem: given a piece of text, predict a R-valued quantity associated with that text.

we use a company's annual financial report to predict the financial risk of investment in that company, as measured empirically by a quantity known as stock return volatility.


In step 2.1, we formalize the feature extraction process as a constrained optimization problem that captures the interdependencies between the features used to represent the sample.

obtaining F (v s , v t ) requires solving an optimization problem.


The Legality Principle constrains the segments that can begin and end syllables to those that appear at the beginning and end of words.

a syllable is not allowed to begin with a consonant cluster that is not found at the beginning of some word, or end with a cluster that is not found at the end of some word (Goslin and Frauenfelder, 2001).


Incremental initialization assigns the initial parse trees t i to sentences w i in order, updating the adaptor grammar as it goes.


Table label resampling is an additional resampling step performed after each Gibbs sweep through the training data in which we resample the parse trees labeling the tables in the adaptor for each X ∈ A.

if the adaptor C X for X ∈ A currently contains m tables labeled with the trees t = (t 1 , .


First, there seem to be qualitative constraints on the relationship between the dependency structure of the words in a sentence and their linear ordering.

this relationship seems to be such that any natural language sentence, together with its dependency structure, should be generable by a mildly context-sensitivity formalism (Joshi, 1985), in particular a linear context-free rewrite system in which the right-hand side of each rule has a distinguished head (Pollard, 1984;Vijay-Shanker et al., 1987;Kuhlmann, 2007).


For the purposes of inference it will be preferable to have a prior that decomposes over levels and segments.

we do not want to have to commit to a particular segmentation at level before segmenting level + 1.


We also contend that they provide convenient building blocks for adding more structure to a summarization model.

we utilize a variation of the hierarchical LDA topic model (Blei et al., 2004) to discover multiple specific 'subtopics' within a document set.


5 We compute ROUGE scores with and without stop words removed from peer and proposed summaries.

we utilize R-1 (recall against unigrams), R-2 (recall against bigrams), and R-SU4 (recall against skip-4 bigrams) 6 .


In the experiment described here, we allow the ASR a larger right context of size ∆ by taking into account at time t the output of the ASR up to time t − ∆ only.

what the ASR hypothesizes about the interval ]t − ∆, t] is considered to be too immature and is discarded, and the hypotheses about the input up to t − ∆ have the benefit of a lookahead up to t. This reduces jitter, which is found mostly to the very right of the incremental hypotheses.


Finally, we presented ways for the online postprocessing of incremental results, looking for ways to improve some of the measures defined, while hurting the other measures as little as possible.

we were interested in generating less wrong hypotheses at the cost of possible short delays.


Voice search is an increasingly popular application of speech recognition to telephony.

in the last two years several companies have come out with systems for local business voice search (LBVS).


However, every entry in the training pronunciation dictionary is a fully diacritized word mapped to a set of possible contextdependent pronunciations.

in the training step, contextual information for each word is available from the transcript, so, for our work, we can use the MADA morphological tagger to obtain the most likely diacritics.


For the 200+ n-gram models we evaluate, we find that the empirical relationship holds with a correlation of 0.9997 where γ is a constant and whereΛ = {λ i } are regularized parameter estimates; i.e., rather than estimating performance for maximum likelihood models as in AIC, we do this for regularized models.

test set cross-entropy can be approximated by the sum of the training set cross-entropy and the scaled sum of the magnitudes of the model parameters.


Thus, our analysis suggests that among our candidates, the best predictor of optimism is simply where γ = 0.938, with this value being independent of domain, training set size, and n-gram order.

the difference between test and training cross-entropy is a linear function of the sum of parameter magnitudes scaled per event.


Minimum discrimination information models  are exponential models with a prior distribution q(y|x): The central issue in performance prediction for MDI models is whether q(y|x) needs to be accounted for.

if we assume q is an exponential model, should its parameters λ q i be included in the sum in eq.


instead of selecting between candidate models once they have been built as in model selection, it is desirable to be able to select between models at the model design stage.


In addition, this transformation can be viewed as shrinking together word n-grams that differ only in w j .

we expect that pairs of n-grams w j−2 w j−1 w j that differ only in w j (belonging to the same class) should have similarλ i .


In summary, we have demonstrated how the tradeoff between training set performance and model size impacts aspects of language modeling as diverse as backoff n-gram features, class-based models, and domain adaptation.

we can frame performance improvements in all of these areas as methods that shrink models without degrading training set performance.


We train our model using empirical Bayesian estimation.

we fix α = 0.1, and we learn the values of word distributions β and β and user distribution γ by maximizing the likelihood of the training data: (Obviously, β is not present in the LinkLDA models.)


( , ) ( , ) This representation is defined through a (possibly stochastic) mapping between each value x X ∈ to each representative value c C ∈ .

formally, this mapping can be characterized by a conditional distribution p(c|x), inducing a soft partitioning of X values, each value of X _is associated with all the codebook elements (C values), with some normalized probability.


The information bottleneck principle determines the distortion measure between the points x and c to be , the Kullback-Leibler divergence (Cover and Thomas, 1991) between the conditional distributions p (y|x) and p(y|c).

the formal optimal solution is given by the following equations which must be solved together.


An important special case is the "hard" clustering case where C is a deterministic function of X.

p(c|x) can only take values of zero or one, This restriction, which corresponds to the limit β → ∞ in Eqs 3 meaning every x X ∈ is assigned to exactly one cluster c C ∈ with a probability of one and to all the others with a probability of zero.


Identifying subjective sentences in a document via offthe-shelf packages is an alternative way of detecting reviews without (additional) human annotations.

the OpinionFinder system (Riloff and Wiebe, 2003;Wiebe and Riloff, 2005) is a state-ofthe-art knowledge-rich sentiment-analysis system.


The goal of this experiment is to get a sense of the best possible accuracy and f-measure numbers using labeled data and the state-of-theart method for text classification, namely, SVMs.

the performance numbers obtained through SVMs and cross-validation can be thought of as realistic "upper bounds" on the performance of content-based review detection.


Our hypothesis is that the syntactic aspects of "framing", as characterized by Entman, involve manipulation of these semantic properties, even when overt opinions are not being expressed.

we propose a connection between syntactic choices and implicit sentiment mediated by the very same semantic properties that linguists have already identified as central when connecting surface expression to underlying meaning more generally.


OPUS features are defined in terms of syntactic dependency relations involving terms in T .

given a set D of syntactic dependency relations, features are of the form t : they are term-dependency pairs extracted from term-dependency-term dependency tuples, preserving whether the term is the head or the dependent in the dependency relation.


We considered several alternatives; the most successful was a simple weighted count of n-gram matches of varying size, appropriately backed-off.


The problems for case frame construction are syntactic and semantic ambiguities.

the resulting parses inevitably contain errors and predicate senses are intrinsically ambiguous.


First, we extract modifier-head examples that had no syntactic ambiguity, and assemble them by coupling a predicate and its closest case component.

we assemble the examples not by predicates, such as tsumu (load/accumulate), but by cou- ples, such as nimotsu-wo tsumu (load baggage) and keiken-wo tsumu (accumulate experience).


Up to the corpus size of 1.6 billion sentences, or 100 billion words, these experimental results still show a steady increase in performance.

we can say that the corpus size of 1.6 billion sentences is not enough to obtain case frames of sufficient coverage.


experimented with changing the weighting scheme, mainly focusing on prioritizing rare vs. frequent words, and has shown significant differences in short-sentence comparison results.

in the sections below we compare the original LSA weighting scheme with our new scheme based on LSAspec: we replace the weight parameter a w and the implicit weight contained in the length of each word vector (by normalizing it) with the specificity value of LSAspec.


The net result is a grammar which only uses the increased context afforded by the TSG when necessary to model the data, and otherwise uses context-free rules.

1 our model learns to use larger rules when the CFG's independence assumptions do not hold.


This can be done using a Dirichlet process (DP) prior.

we define the distribution of elementary tree e with root non-terminal symbol c as where P 0 (•|c) (the base distribution) is a distribution over the infinite space of trees rooted with c, and α c (the concentration parameter) controls the model's tendency towards either reusing elementary trees or creating novel ones as each training instance is encountered (and consequently, the tendency to infer larger or smaller sets of elementary trees from the observed data).


Indeed, coarse-to-fine is even more effective when a hierarchy of successive approximations is used (Charniak et al., 2006;Petrov and Klein, 2007).

petrov and Klein (2007) generate a sequence of approximations to a highly subcategorized grammar, parsing with each in turn.


Another line of work has explored A * search methods, in which simpler problems are used not for pruning, but for prioritizing work in the full search space (Klein and Manning, 2003a;Haghighi et al., 2007).

klein and Manning (2003a) investigated A * for lexicalized parsing in a factored model.


If h is consistent, then the A * algorithm guarantees that whenever an edge comes off the agenda, its weight is its true Viterbi inside cost.

this guarantee implies that the first edge representing the root I(R, 0, n) will be scored with the true Viterbi score for the sentence.


Each grammar G t has an inventory of symbols Σ t , hereafter denoted with capital letters.

each grammar has a distinguished terminal symbol T i t for each word T i in the input and a root symbol R t .


While there is only one agenda, it is useful to imagine several charts, one for each type of edge and each grammar level.

outside edges from one level of the hierarchy are the source of completion costs (heuristics) for inside edges at the next level.


Therefore, we cannot carry out this deduction until the required outside edge is present in the previous level's chart.

fine inside deductions wait for the relevant coarse outside edges to be popped.


As in standard A * , inside edges are placed on the agenda with a priority equal to their path cost (inside score) and some estimate of their completion cost (outside score), now taken from the previous projection rather than a black box.

the priority function takes the form p(e) = β e + αē , whereē is the outside version of e one level previous in the hierarchy.


In standard CTF, we exhaustively parse in each projection level, but skip edges whose projections in the previous level had sufficiently low scores.

an edge e in the grammar G t will be skipped entirely if its projection e in G t−1 had a low max marginal: αē + β e , that is, the score of the best tree containing e was low compared to the score best overall root derivation β R .


However, when edges from grammars of varying size are processed on the same agenda, the number of successor edges per edge popped changes depending on what grammar the edge was constructed from.

edges in more refined grammars are more expensive than edges in coarser grammars.


While this ILP approach and Ng's (2004) approach to improving the output of an anaphoricity classifier both result in increased coreference performance, they have complementary strengths and weaknesses.

ng's approach can directly optimize the desired coreference evaluation metric, but by treating the coreference system as a black box during the optimization process, it does not exploit the potentially useful pairwise probabilities provided by the coreference classifier.


Ng and Cardie (N&C) do not attempt to improve P A , simply using the anaphoricity information it provides as hard constraints for coreference resolution.

the coreference system resolves only those mentions that are determined as anaphoric by P A , where a mention is classified as anaphoric if the classification threshold is at least 0.5.


In essence, Ng's approach to improving P A treats the coreference system as a black box, merely selecting the value for c that yields the best score according to the desired coreference evaluation metric on the held-out data.

unlike some of the anaphoricity determination approaches discussed later on, this approach does not attempt to coordinate the anaphoricity decisions and the pairwise coreference decisions.


Among the five anaphoricity determination approaches, Luo's (2007) is the only one where anaphoricity information is exploited as soft constraints by the coreference model, P C .

luo's algorithm attempts to find the most probable coreference partition of a given set of mentions.


ILP provides a framework for jointly determining anaphoricity and coreference decisions for a given set of mentions based on the probabilities provided by P A and P C , such that the resulting joint decisions satisfy the desired constraints while respecting as much as possible the probabilistic decisions made by the independently-trained P A and P C .

an ILP program is composed of an objective function to be optimized subject to a set of linear constraints, and is created for each test text D as follows.


In addition, each variable is associated with an assignment cost.

let c C i,j = − log(P C (m i , m j )) be the cost of setting be the complementary cost of setting x i,j to 0.


As mentioned in the introduction, our graph-cutbased approach to anaphoricity determination is motivated by Ng's (2004) and the ILP approach, aiming to combine the strengths of the two approaches.

like Ng (2004), our approach allows direct optimization of the desired coreference evaluation metric; and like the ILP approach, our approach co-ordinates anaphoricity decisions and coreference decisions by exploiting the pairwise probabilities provided by a coreference model.


The ultimate goal is to use the mincut finder to partition a given set of mentions into two subsets, so that our coreference system will attempt to resolve only those mentions that are in the subset corresponding to ANAPHORIC.

the resulting anaphoricity information will be used to identify and filter non-anaphoric mentions prior to coreference resolution.


Step 2: Mimicking Ng (2004) One way to "improve" G is to make it functionally equivalent to Ng's (2004) approach.

our goal in Step 2 is to modify the edge weights in G (without adding new edges or nodes) such that the mincut finder classifies a node n as anaphoric if and only if P A (m n ) ≥ c for some c ∈ [0, 1].


However, note that the minimum cut algorithm does not maintain the notion of directionality that would allow one to determine that a discourse-new mention (i.e., the first mention of a coreference chain) is not anaphoric.

the algorithm tends to classify all members of a coreference chain, including the first mention, as anaphoric.


In this paper we present the first steps in producing an automatically generated, readily consumable, technical survey.

we explore the combination of citation information and summarization techniques.


First, the languages may be related "genetically."

they may have all derived from a common ancestor language.


In this paper, we attempt to provide a statistical answer to some of these questions.

we develop a Bayesian model of typology that allows for, but does not force, the existence of linguistic areas.


By having the parameter feature-specific, we express the fact that some features may be more or less likely to be shared than others.

a high value of π f would mean that feature f is easily shared areally, while a low value would mean that feature f is hard to share.


In the first, we attempt to predict unknown feature values.

we hide an addition 10% of the feature values in the WALS data and fit a model  to the remaining 90%.


2 In his presentation, the adapation is done through feature augmentation.

for each feature in the original version, a new version is created for each domain, as well as a general, domain-independent version of the feature.


Our full dependency parsing results can be found in Table 3.

firstly, we found that DAUME07, which had outperformed the ALL DATA baseline for the sequence modeling task, performed worse than the 7 all the other domains use the "new" Penn Treebank annotation style, whereas the WSJ data is still in the "traditional" annotation style, familiar from the past decade's work in Penn Treebank parsing.


Standard results from the stochastic approximation literature state that ∞ k=0 η k = ∞ and ∞ k=0 η 2 k < ∞ are sufficient to guarantee convergence to a local optimum.

if we take η k = (k + 2) −α , then any 0.5 < α ≤ 1 is valid.


Mini-batch size m We can add some stability to sEM by updating on multiple examples at once instead of just one.

partition the n examples into mini-batches of size m and run sEM, treating each mini-batch as a single example.


Another line of research for keyword extraction has adopted graph-based methods similar to Google's PageRank algorithm (Brin and Page, 1998).

(Wan et al., 2007) attempted to use a reinforcement approach to do keyword extraction and summarization simultaneously, on the assumption that important sentences usually contain keywords and keywords are usually seen in important sentences.


The final weight for a word node depends on its connection to other words (W-W graph) and other sentences (W-S graph); similarly, the weight for a sentence node is dependent on its connection to other sentences (S-S graph) and other words (S-W graph).

where u and v are the weight vectors for sentence and word nodes respectively, U, V, W,Ŵ represent the S-S, W-W, S-W, and W-S connections.


2 Conversational Floor as a Finite-State Machine 2.1 6-state finite state models of turn-taking In the 1960's and early 1970's, several researchers proposed models to explain the rhythmic turn-taking patterns in human conversation.

jaffe and Feldstein (1970) studied the mean duration of pauses, switching pauses (when a different speaker takes the floor), simultaneous speech, and (singlespeaker) vocalizations in recorded dyadic conversations.


This representation allows us to formalize a wide variety of turn-taking phenomena in a unified framework.

there are 4 types of 2-step transitions from a single-floor-holder state (SY ST EM or U SER) to another (or the same) single-floor-holder state, which represent typical turn-taking phenomena: Turn transitions with gap are the most common way the floor goes from one participant to the other.


For instance, after a system prompt, if the floor remains free for a certain amount of time, the system attempts to re-establish the communication with the user, as follows: The opposite also happens when the system is to slow to respond to the user: While all the transitions above were described as deterministic, the actual state of the model is not fully observable.

while the system knows whether its claiming the floor or not, it can only believe with some degree of uncertainty that the user does so.


The preceding context within the sentence, however, should rule out this interpretation because the player appears within a PP and hence should not be able to be the subject of a new sentence (Figure 1, right).

given the preceding context, the player tossed a frisbee must begin a reduced RC, such that there is no local ambiguity.


While Tabor and Hutchins (2004) work out these questions in detail for the types of sentences they model, it is unclear how the model could be scaled up to make predictions for arbitrary types of sentences.

there is no principled system for setting the three types of parameters mentioned, and thus no clear interpretation of their values.